AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text

Chinese named entity recognition of wheat diseases and pests is an initial and key step in constructing knowledge graphs. In the field of wheat diseases and pests, there are problems, such as lack of training data, nested entities, fuzzy entity boundaries, diverse entity categories, and uneven entit...
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Autor*in:	Demeng Zhang [verfasserIn] Guang Zheng [verfasserIn] Hebing Liu [verfasserIn] Xinming Ma [verfasserIn] Lei Xi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Chinese named entity recognition wheat diseases and pests data augmentation ALBERT-BiLSTM-CRF rules amendment

Übergeordnetes Werk:	In: Agriculture - MDPI AG, 2012, 13(2023), 6, p 1220
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:6, p 1220

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agriculture13061220

Katalog-ID:	DOAJ094228221

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520			\|a Chinese named entity recognition of wheat diseases and pests is an initial and key step in constructing knowledge graphs. In the field of wheat diseases and pests, there are problems, such as lack of training data, nested entities, fuzzy entity boundaries, diverse entity categories, and uneven entity distribution. To solve the above problems, two data augmentation methods were proposed to expand sentence semantic information on the premise of fully mining hidden knowledge. Then, a wheat diseases and pests dataset (WdpDs) for Chinese named entity recognition was constructed containing 21 types of entities and its domain dictionary (WdpDict), using a combination of manual and dictionary-based approaches, to better support the entity recognition task. Furthermore, an automated Wdp Chinese named entity recognition model (AWdpCNER) was proposed. This model was based on ALBERT-BiLSTM-CRF for entity recognition, and defined specific rules to calibrate entity boundaries after recognition. The model fusing ALBERT-BiLSTM-CRF and rules amendment achieved the best recognition results, with a precision of 94.76%, a recall of 95.64%, and an F1-score of 95.29%. Compared with the recognition results without rules amendment, the precision, recall, and F1-score was increased by 0.88 percentage points, 0.44 percentage points, and 0.75 percentage points, respectively. The experimental results showed that the proposed model could effectively identify Chinese named entities in the field of wheat diseases and pests, and this model achieved state-of-the-art recognition performance, outperforming several existing models, which provides a reference for other fields of named entities recognition such as food safety and biology.
650		4	\|a Chinese named entity recognition
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language	English
source	In Agriculture 13(2023), 6, p 1220 volume:13 year:2023 number:6, p 1220
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container_title	Agriculture
authorswithroles_txt_mv	Demeng Zhang @@aut@@ Guang Zheng @@aut@@ Hebing Liu @@aut@@ Xinming Ma @@aut@@ Lei Xi @@aut@@
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callnumber-first	S - Agriculture
author	Demeng Zhang
spellingShingle	Demeng Zhang misc S1-972 misc Chinese named entity recognition misc wheat diseases and pests misc data augmentation misc ALBERT-BiLSTM-CRF misc rules amendment misc Agriculture (General) AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text
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topic_title	S1-972 AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text Chinese named entity recognition wheat diseases and pests data augmentation ALBERT-BiLSTM-CRF rules amendment
topic	misc S1-972 misc Chinese named entity recognition misc wheat diseases and pests misc data augmentation misc ALBERT-BiLSTM-CRF misc rules amendment misc Agriculture (General)
topic_unstemmed	misc S1-972 misc Chinese named entity recognition misc wheat diseases and pests misc data augmentation misc ALBERT-BiLSTM-CRF misc rules amendment misc Agriculture (General)
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title	AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text
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title_full	AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text
author_sort	Demeng Zhang
journal	Agriculture
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author_browse	Demeng Zhang Guang Zheng Hebing Liu Xinming Ma Lei Xi
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title_sort	awdpcner: automated wdp chinese named entity recognition from wheat diseases and pests text
callnumber	S1-972
title_auth	AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text
abstract	Chinese named entity recognition of wheat diseases and pests is an initial and key step in constructing knowledge graphs. In the field of wheat diseases and pests, there are problems, such as lack of training data, nested entities, fuzzy entity boundaries, diverse entity categories, and uneven entity distribution. To solve the above problems, two data augmentation methods were proposed to expand sentence semantic information on the premise of fully mining hidden knowledge. Then, a wheat diseases and pests dataset (WdpDs) for Chinese named entity recognition was constructed containing 21 types of entities and its domain dictionary (WdpDict), using a combination of manual and dictionary-based approaches, to better support the entity recognition task. Furthermore, an automated Wdp Chinese named entity recognition model (AWdpCNER) was proposed. This model was based on ALBERT-BiLSTM-CRF for entity recognition, and defined specific rules to calibrate entity boundaries after recognition. The model fusing ALBERT-BiLSTM-CRF and rules amendment achieved the best recognition results, with a precision of 94.76%, a recall of 95.64%, and an F1-score of 95.29%. Compared with the recognition results without rules amendment, the precision, recall, and F1-score was increased by 0.88 percentage points, 0.44 percentage points, and 0.75 percentage points, respectively. The experimental results showed that the proposed model could effectively identify Chinese named entities in the field of wheat diseases and pests, and this model achieved state-of-the-art recognition performance, outperforming several existing models, which provides a reference for other fields of named entities recognition such as food safety and biology.
abstractGer	Chinese named entity recognition of wheat diseases and pests is an initial and key step in constructing knowledge graphs. In the field of wheat diseases and pests, there are problems, such as lack of training data, nested entities, fuzzy entity boundaries, diverse entity categories, and uneven entity distribution. To solve the above problems, two data augmentation methods were proposed to expand sentence semantic information on the premise of fully mining hidden knowledge. Then, a wheat diseases and pests dataset (WdpDs) for Chinese named entity recognition was constructed containing 21 types of entities and its domain dictionary (WdpDict), using a combination of manual and dictionary-based approaches, to better support the entity recognition task. Furthermore, an automated Wdp Chinese named entity recognition model (AWdpCNER) was proposed. This model was based on ALBERT-BiLSTM-CRF for entity recognition, and defined specific rules to calibrate entity boundaries after recognition. The model fusing ALBERT-BiLSTM-CRF and rules amendment achieved the best recognition results, with a precision of 94.76%, a recall of 95.64%, and an F1-score of 95.29%. Compared with the recognition results without rules amendment, the precision, recall, and F1-score was increased by 0.88 percentage points, 0.44 percentage points, and 0.75 percentage points, respectively. The experimental results showed that the proposed model could effectively identify Chinese named entities in the field of wheat diseases and pests, and this model achieved state-of-the-art recognition performance, outperforming several existing models, which provides a reference for other fields of named entities recognition such as food safety and biology.
abstract_unstemmed	Chinese named entity recognition of wheat diseases and pests is an initial and key step in constructing knowledge graphs. In the field of wheat diseases and pests, there are problems, such as lack of training data, nested entities, fuzzy entity boundaries, diverse entity categories, and uneven entity distribution. To solve the above problems, two data augmentation methods were proposed to expand sentence semantic information on the premise of fully mining hidden knowledge. Then, a wheat diseases and pests dataset (WdpDs) for Chinese named entity recognition was constructed containing 21 types of entities and its domain dictionary (WdpDict), using a combination of manual and dictionary-based approaches, to better support the entity recognition task. Furthermore, an automated Wdp Chinese named entity recognition model (AWdpCNER) was proposed. This model was based on ALBERT-BiLSTM-CRF for entity recognition, and defined specific rules to calibrate entity boundaries after recognition. The model fusing ALBERT-BiLSTM-CRF and rules amendment achieved the best recognition results, with a precision of 94.76%, a recall of 95.64%, and an F1-score of 95.29%. Compared with the recognition results without rules amendment, the precision, recall, and F1-score was increased by 0.88 percentage points, 0.44 percentage points, and 0.75 percentage points, respectively. The experimental results showed that the proposed model could effectively identify Chinese named entities in the field of wheat diseases and pests, and this model achieved state-of-the-art recognition performance, outperforming several existing models, which provides a reference for other fields of named entities recognition such as food safety and biology.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700
container_issue	6, p 1220
title_short	AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text
url	https://doi.org/10.3390/agriculture13061220 https://doaj.org/article/7f7e9684636d4d428abb4db608776bc4 https://www.mdpi.com/2077-0472/13/6/1220 https://doaj.org/toc/2077-0472
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author2	Guang Zheng Hebing Liu Xinming Ma Lei Xi
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doi_str	10.3390/agriculture13061220
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up_date	2024-07-03T21:59:52.168Z
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AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text

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